Where \(\Theta ^{(k)}(x)\)  is a function that extracts \(k\)  random patches of size \(\frac{\max (H,W)}{8}\)  from \(x\) , and \(H,W\)  are the height and width of \(x\)  respectively. \(\mathcal {D}\)  is a discriminator that evaluates whether a single patch (\(\Theta ^{(1)}(O)\)  or \(\Theta ^{(1)}(S)\) ) comes from the same image as 4 patches extracted from the style image (\(\Theta ^{(4)}(S)\) ). We use the same discriminator architecture and discriminator training described in [1]}, where further details can be found. We fit the parameters of our model, \(\theta _\mathcal {G}\)  to minimize the full objective:
